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Found 3 papers, 2 papers with code
Tandem Blocks in Deep Convolutional Neural Networks
Due to the success of residual networks (resnets) and related architectures, shortcut connections have quickly become standard tools for building convolutional neural networks.
Forward Thinking: Building and Training Neural Networks One Layer at a Time
We present a general framework for training deep neural networks without backpropagation.
Forward Thinking: Building Deep Random Forests
We present a general framework called forward thinking for deep learning that generalizes the architectural flexibility and sophistication of deep neural networks while also allowing for (i) different types of learning functions in the network, other than neurons, and (ii) the ability to adaptively deepen the network as needed to improve results.
